Edge AI for Real-Time Video Analytics in Public Safety

Abstract

Edge Artificial Intelligence (Edge AI) has emerged as a powerful technology for enhancing real-time video analytics in public safety applications. By processing video data directly at the edge of the network, close to the data source, Edge AI reduces latency, minimizes bandwidth consumption, and improves data privacy compared to traditional cloud-based solutions. This technology enables rapid detection and analysis of events such as suspicious activities, traffic violations, crowd congestion, unauthorized access, and emergency situations. Real-time video analytics powered by Edge AI supports faster decision-making and more effective responses from law enforcement agencies, emergency services, and security personnel. Furthermore, the integration of advanced deep learning algorithms with edge computing devices facilitates continuous monitoring and intelligent surveillance even in environments with limited network connectivity. This paper explores the architecture, applications, benefits, challenges, and future prospects of Edge AI for real-time video analytics in public safety. The study highlights how Edge AI contributes to creating safer communities by enhancing situational awareness, operational efficiency, and proactive threat detection while addressing concerns related to privacy, scalability, and computational constraints.

Introduction

This paper focuses on the development of an Edge AI-based real-time video analytics system for public safety and surveillance applications, addressing the limitations of traditional cloud-based and human-dependent monitoring systems.

1. Introduction

The rapid expansion of surveillance systems in smart cities, transportation networks, and critical infrastructure has led to massive volumes of video data. Traditional monitoring methods rely heavily on human operators, making them inefficient and error-prone. Cloud-based video analytics, while automated, often suffers from:

• High latency
• Bandwidth limitations
• Privacy and security concerns

To overcome these issues, the study highlights Edge AI, where video processing occurs locally on devices such as smart cameras, drones, and edge servers. This reduces delay, minimizes data transmission, and improves real-time decision-making for public safety.

2. Literature Review

Existing research shows strong progress in Edge AI-based surveillance systems:

• Edge AI enables real-time processing for crime detection, traffic monitoring, crowd analysis, and emergency response.
• Studies (e.g., Hu et al., Xu et al.) confirm that edge-based systems significantly reduce latency and bandwidth usage.
• Deep learning models like CNNs and YOLO have been successfully deployed on edge devices for real-time detection.
• Systems such as SurveilEdge and fog-based architectures demonstrate improved efficiency in large-scale surveillance.
• IoT and embedded AI systems enhance smart city applications, including parking detection, vehicle tracking, and disaster response.

Overall, research confirms Edge AI as a key technology for next-generation surveillance systems.

3. Research Gaps

Despite advancements, several limitations remain:

• High computational demands for edge devices
• Reduced accuracy in complex real-world conditions (low light, crowding, weather issues)
• Insufficient privacy-preserving and secure data handling methods
• Lack of unified systems integrating multiple surveillance functions
• Scalability challenges in large smart city deployments

These gaps highlight the need for more efficient, scalable, and privacy-aware Edge AI frameworks.

4. Methodology

The proposed system is an Edge AI-based real-time surveillance framework consisting of four main layers:

1. Video Acquisition Layer

• CCTV cameras, drones, and sensors capture live video streams.

2. Edge Processing Layer

• Devices like NVIDIA Jetson and Raspberry Pi process video locally.
• Preprocessing includes resizing, normalization, and noise reduction.

3. AI Analytics Layer

• Deep learning models (e.g., YOLO) perform:
  • Object detection
  • Human activity recognition
  • Facial recognition
  • Anomaly detection

4. Alert and Response Layer

• Detected threats trigger automatic alerts.
• Notifications are sent to control centers and security personnel for immediate action.

5. Data and Model Development

• Datasets include CCTV footage, traffic videos, crowd behavior data, and public surveillance recordings.
• Data is labeled into normal and abnormal events.
• Preprocessing includes frame extraction, augmentation, and enhancement.
• A supervised deep learning model is trained to detect:
  • People
  • Vehicles
  • Suspicious objects
  • Abnormal activities

The trained model is deployed on edge devices for real-time inference.

6. Performance Evaluation

The system is evaluated using:

• Accuracy
• Precision
• Recall
• F1-score
• Latency
• Frames per second (FPS)

These metrics assess both detection quality and real-time performance efficiency.

7. Expected Outcomes

The proposed Edge AI surveillance system is expected to deliver:

• Real-time threat detection
• Reduced bandwidth usage
• Low-latency processing
• Improved privacy and security
• Faster emergency response
• Better scalability for smart cities

Conclusion

Edge AI for Real-Time Video Analytics has emerged as a transformative technology for enhancing public safety and security. By combining the capabilities of artificial intelligence with edge computing, the proposed system enables real-time analysis of video data directly at the source, reducing latency, bandwidth consumption, and dependence on cloud infrastructure. The study demonstrates that Edge AI can effectively support various public safety applications, including crime detection, crowd monitoring, traffic management, anomaly detection, and emergency response. The results indicate improved accuracy, faster decision-making, enhanced privacy, and efficient resource utilization compared to traditional surveillance approaches. Although challenges such as limited edge device resources, environmental variations, and privacy concerns remain, ongoing advancements in AI, IoT, and 5G technologies are expected to address these limitations. Overall, Edge AI-based real-time video analytics provides an intelligent, scalable, and cost-effective solution for modern public safety systems, contributing significantly to the development of safer and smarter communities.

References

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Copyright

Copyright © 2026 Prithvi Siva Sankar Shunmuga Sundaram , R Gokilamani, Sureshkumar Somayajula. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.